A Survey on Several Technical Methods for Selecting Initial Cluster Centers in K-Means Clustering Algorithm

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Provided by: International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Topic: Data Management
Format: PDF
Drastic growth of digital data is an emerging area of concern which has led to concentration of data mining technique. The actual data mining task is the programmatic or semi-programmatic analysis of large quantities of data to extract hidden interesting patterns such as groups of data records, which is usually referred as cluster analysis or clustering. Clustering is the classification of data objects into different groups, or more incisively the partitioning of a data set into subsets (clusters), so that the data objects of each cluster share common characteristics. Several clustering algorithms have been proposed among which k-means is one of the simplest unsupervised learning algorithm that will solve the well-known clustering problem.
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